A data correction method for allowing any observer to satisfactorily perform height correction on an image is provided. Specifically, a data correction method for correcting height of a plurality of pieces of measurement data by using image data acquired by a scanning probe microscope is disclosed. In this data correction method, a computer extracts a reference plane region from the image data, selects three pieces of measurement data, from the pieces of measurement data, at three points in the extracted reference plane region as first to third reference point data, and performs height correction on the other pieces of measurement data on the assumption that the first to third reference point data have the same height.

An image data processing method includes receiving, from an image sensor, frame image data of a frame at a first resolution, reducing a resolution of the frame image data to a second resolution, performing image recognition on the frame image data to determine one or more regions of interest (ROI) and a priority level of each of the one or more ROIs, and extracting portions of the frame image data corresponding to the one or more ROIs. The method further includes modifying a resolution of the portions of the frame image data corresponding to the one or more ROIs based on the priority level of the ROIs, and combining the resolution-modified portions of the frame image data corresponding to the one or more ROIs with the frame image data at the second resolution to generate output frame image data.

An image processing apparatus comprises: an acquisition unit that acquires images of a plurality of consecutive frames; a first composite unit that performs lighten composite on the images; a second composite unit that performs darken composite on the images by two consecutive frames, and subtracts an image of a frame before or after the images of the two frames subjected to the darken composite from a result of the darken composite to generate a subtraction image; a third composite unit that performs lighten composite on subtraction images obtained by the second composite unit; and an addition unit that adds a first image obtained by performing the lighten composite on the images by the first composite unit and a second image obtained by performing lighten composite on the subtraction images.

This application provides an image recognition method, a storage medium, and a computer device. The method includes: obtaining a to-be-recognized image; preprocessing the to-be-recognized image, to obtain a preprocessed image; obtaining, through a first submodel in a machine learning model, a first image feature corresponding to the to-be-recognized image, and obtaining, through a second submodel in the machine learning model, a second image feature corresponding to the preprocessed image; and determining, according to the first image feature and the second image feature, a first probability that the to-be-recognized image belongs to a classification category corresponding to the machine learning model. It may be seen that, the solutions provided by this application can improve recognition efficiency and accuracy.

Disclosed are an image tuning device and an image tuning method. The image tuning method includes the following steps: dividing an image area into a plurality of blocks for executing brightness adjustment individually, in which the blocks include a target block and at least one neighboring block; receiving pixel data of the target block to calculate a target block brightness value; receiving pixel data of the at least one neighboring block to calculate at least one neighboring block brightness value; calculating a calculated brightness value of a target pixel within the target block according to the target block brightness value and the at least one neighboring block brightness value; and generating an adjusted brightness value of the target pixel by adjusting an original brightness value of the target pixel according to the calculated brightness value.

Image pair co-registration systems and methods include receiving a pair of multi-modal images, defining a parametric deformation model, defining a loss function that is minimized when the pair of images are aligned, and performing a multi-scale search to determine deformation parameters that minimize the loss function. The optimized deformation parameters define an alignment of the pair of images. The pair of images may include visible spectrum image and an infrared image. The method further includes resizing the visible spectrum image to match the infrared image, applying at least one lens distortion correction model, and normalizing a dynamic range of each of the pair of images. The multi-scale search may further include resizing the pair of images to a current processing scale, applying adaptive histogram equalization to the pair of images to generate equalized images, applying Gaussian Blur to the equalized images, and optimizing the deformation parameters.

An image processing method acquires an image, restores a saturated region in which a pixel in the image has a first reference value based on a first illuminance component of the image, enhances a dark region in which a value of a pixel in the image is less than a second reference value based on the restored saturated region and the first illuminance component, and outputs a dark region-enhanced image.

An automated fire detection system includes a distributed network of standalone sensor units having mulifunctional capability to detect wildfires at their earliest stage. Multiple modes of verification are employed, including thermal imaging, spectral analysis, near infrared and long-wave infrared measurements, measurements of the presence and/or concentration of smoke, and sensing local temperature and humidity and wind speed and direction. A dedicated algorithm receives all data from the network and determines the location of flames from the imaging sensors, combined with the smoke, temperature, humidity, and wind measurements at every dispersed device.

Disclosed herein is an image processing apparatus including: an image data acquisition section configured to acquire data of an image; a luminance conversion section configured to convert a luminance range composing a pixel value of the image so as to correspond to the luminance range capable of an output as a display image; and an output section configured to output the data of the image in which a luminance after the conversion is defined as the pixel value. The luminance conversion section acquires information regarding a linear luminance range capable of an output in the luminance that is proportional to a setting luminance in a display and determines a conversion rule at least on a basis of the information.

An image processing apparatus comprises: first and generation units that generate color signal components for luminance and color signal components for color difference, respectively, in a first color space from an input image signal; an integration unit that integrates the color signal components for luminance and color difference to generate color signal components common for luminance and color difference; a processing unit that performs nonlinear processing on the color signal components common for luminance and color difference; a separation unit that generates luminance and color difference signals in a second color space from the signal that has undergone the nonlinear processing; and a band limiting unit that performs band limitation processing on the color difference signals. A characteristic of the band limitation processing is set on the basis of frequency characteristics of the luminance color difference signals.